1. Field of the Invention
The present invention relates to the field of automated decision making, in particular to automatically determining instructional content to be presented to individual students.
2. Description of the Prior Art
To date, the preparation of effective training materials for students has been a manual, labor-intense process. It typically starts with manual task analysis or knowledge elicitation sessions, and these require a fair amount of time from both training professionals and subject matter experts (SMEs). The materials developed from those sessions are then manually transformed into a set of knowledge, skills, and abilities (KSAs) and training objectives (TOs) and instructional content, lectures, scenarios, simulations, etc., are subsequently developed for each. In the best of cases, one or more performance metrics manually developed for each TO, and an interpretation for those metrics and assessments—is also manually developed. Even though the training content, metrics, and assessments differ between didactic and experiential training environments, the development process is quite similar and equally labor intensive.
This approach requires extensive time and effort to execute, it also imposes unfortunate limits on the resulting training material. Because the process is labor intensive, training objectives are rarely tailored to individual needs; and performance metrics and assessments seldom do more than simply identify when or where students struggle. They almost never provide the individualized diagnostic power that would make for optimum training.
There is also increasing evidence suggesting that individualized training, in the form of tailored content, metrics, and assessments, can yield considerably more powerful results. One of the conventional ways of adapting training to the needs of individual trainees, using Intelligent Tutoring Systems (ITS), requires building individual models of how each individual trainee learns the required KSAs and then applying that model to the training regimen. To date, this has been successfully accomplished only with extensive effort.
Instructional design is entering a period of transformation, one in which this intellect-driven process becomes increasingly data-driven with some data-driven aids for instructional design being considered to overcome some of the above challenges. Research concerning such aids is growing (cf., the International Educational Data Mining Society) as data sources arise from intelligent tutoring systems (cf., the PSLC DataShop), serious games and simulations, and internet courses.